The present application is related generally to machine vision vehicle service systems, and in particular, to a machine vision vehicle service system including an illumination source for projecting a pattern of light onto surfaces within the field of view of an associated imaging sensor, and to a method for computing an axis of rotation of an illuminated rotating body, such as a vehicle wheel, moving through the field of view.
Contactless measurement systems for use in vehicle wheel service procedures, such as wheel alignment measurements and vehicle inspection procedures offer advantages in terms of ease of use, efficiency, and potentially in terms of measurement accuracy over the standards set by the use of conventional wheel-mounted sensors or even machine-vision vehicle service systems employing wheel-mounted optical targets. Both conventional wheel-mounted sensors and wheel-mounted optical targets require an operator to move around a vehicle undergoing a service or inspection procedure to mount and dismount either the sensors or targets. This mounting and dismounting process takes time, and may introduce a source of error into resulting measurements if the sensors or targets are not mounted or compensated properly for the presence of mounting runout.
Contactless measurement systems which utilize imaging sensors to acquire images of the wheels of a vehicle undergoing service, either while the wheels are stationary or while the vehicle is in motion, have the potential to increase the efficiency of a vehicle service or inspection procedure by eliminating some of the prerequisite setup up steps necessary before actual measurements can be acquired using conventional sensors or targets. Similarly, by avoiding the use of wheel clamps or other attachment devices, a potential source of measurement error is eliminated by contactless measurement systems. However, contactless measurement systems are generally not sufficiently advanced so as to always be able to acquire necessary vehicle wheel assembly measurements (i.e. spatial positions and orientations) by simply acquiring an image of an unaltered vehicle wheel in ambient light. This is due in part to the wide variety of vehicle wheel configurations, surfaces, reflectivity, and lighting conditions, etc. which may be encountered when attempting to acquire measurements using a contactless measurement system. Accordingly, some contactless machine vision vehicle service systems utilize an illumination source to project a light, in the form of a pattern such as points or stripes, onto the surface of the vehicle wheel assembly to be observed, such as shown in U.S. Pat. No. 7,454,841 B2 issued to Burns, Jr. et al. on Nov. 25, 2008. The resulting images can be processed to evaluate the distortion or effect of the vehicle wheel assembly surfaces on the projected pattern, from which wheel assembly spatial position and orientation data, such as an axis of rotation, can be extracted.
Previous approaches to identifying the axis of rotation for a vehicle wheel assembly from a series of images acquired by a machine vision vehicle service system have employed a number of techniques. One method, seeks to fit each acquired image to a surface model of a vehicle wheel assembly, and then compare the surface model parameters associated with each of the fitted images to determine the axis of rotation between the surface models. The difficulty in this method is in the establishment of a surface model to match a relatively unknown object (i.e., vehicle wheel assembly), and the fact that the process is computationally expensive. Other methods attempt to determine an axis of symmetry for a point cloud of observed points in an image, and then track that axis of symmetry through multiple images of the wheel assembly at different rotational positions to determine an axis of rotation. However, the axis of symmetry can be easily biased by non-uniformity in the density of the data points on different parts of the wheel assembly, especially if there is a bias such that one side of the wheel assembly always has more points than the other, due to lighting effects or glare. If this type of bias occurs then the axis of symmetry for the point cloud of observed points will not rotate as the wheel assembly rotates, introducing errors into the determination of the axis of rotation.
Accordingly, it would be advantageous to provide a contactless machine vision vehicle service system with a method for processing acquired images of a wheel assembly illuminated by a projected pattern without necessitating the establishment of a complex surface model for each image, which is not computationally expensive, and which is relatively insensitive to biases introduced by a non-uniformity in acquired points of data form each image.